r. mohammadigol

Food security, which is directly related to the health of people, has always been a concern of all nations. Eggs are consumed in many food industries and are in the daily diet of many people, so detection their freshness is very important. In this study, the capability of the acoustic system as a non-destructive method for egg freshness detection was investigated. Samples were stored at room temperature for 1, 4, 7, 10, 13 and 16 days. After data collection, all audio signals were converted to images, using spectrogram. In this study, the freshness of samples was evaluated using two criteria; Haugh unit and air cell height as a destructive test. The results of destructive test showed that all samples stored for 16 and 13 days and also 80% of samples stored for 10 days faced with quality losses during storage. According to grading criteria, these samples were considered as unfresh eggs. Therefore, the samples were divided into two groups: fresh eggs (stored for 1, 4 and 7 days) and unfresh (stored for 10, 13 and 16 days). Four pre-trained deep learning networks AlexNet, VGGNet, GoogLeNet and ResNet were used in this study among which ResNet had the best classification accuracy with an average of 71.5%.

The increasing development and manufacturing of portable optical spectroscopy equipment indicates that techniques based on optical properties are highly effective. For this reason, the use of optical spectroscopy equipment in the rapid test detection of food fraud is still favored by food industry activists. In the present study, modeling of near-infrared spectra in the 950- 1650 nm region was investigated by principal components analysis to discriminate chickpea flour samples from wheat/split pea flours mixed samples on 5, 10, 20, and 30 mixing (weight) percentages. The scatter plots of the spectra in the coordinates of the principal components showed that the mentioned method has been successful in discriminating pure chickpea flour from mixed with split pea/wheat flour. Applying standard normal distribution preprocessing to the spectral data improved the discrimination between the spectra acquiring from different wheat/split pea flour mixing percentages. Most of the differentiating spectral signs were observed in the spectral regions of 1600 -1480 and 1000 - 1330 nm. Therefore, the mentioned bands were considered important in terms of determining the indices of wheat or split pea flour in chickpea flour based on the difference in the percentage of light reflection. The results indicated that it is possible to use the fast and non-destructive method of near-infrared spectroscopy along with the principal components analysis technique in discriminating pure chickpea flour with wheat/split pea flour mixed as an alternative to laboratory methods.

In the confectionary and food industries, chickpea flour has a well-known situation and is at high risk of food fraud when economical issues are concerned. Low prices of wheat and split pea wastes flours compared to chick pea flour are the reasons that these materials are used as common frauds. Demanding of non-destructive methods of quality evaluation and also the increasing trend of the development and production of functional and portable optical equipment were the reasons why this research has been conducted. In this research, the potential of the spectroscopy (420-900 nm) with principle components analysis technique (PCA) and common preprocessing methods to discriminate the samples of chick pea, wheat and split pea flours on 5, 10, 20 and 30% mixing percentage has been studied. The mentioned method on detection of the split pea flour at 30% mixing percentage and lower was unsuccessful but on discrimination of the wheat flour at 30 and 20% mixing percentage was successful and on detection of 5 and 10% by applying preprocessing (SNV/MSC) was successful. The result indicated that there has been a possibility to define an index based on spectral data to detect the wheat flour in chick pea flour in 430-480 nm band, therefore there is a potential to replace experimental methods with fast and nondestructive spectroscopy (420-900 nm) with PCA.

Garlic (Allium sativum L.) is an important Allium crop in the world. Due to its therapeutic properties, it was cultivated in many countries. Furthermore, garlic is usually used as a flavoring agent; it may be used in the shape of powder or granule as a valuable condiment for foods. In addition to its use in food products, it was also widely used as an anticancer agent. Shallot (Allium hiertifolium Boiss. L) is a perennial and bulbous plant. It is from Alliaceae family and is an important medicinal plant. The shallot is native of Iran, and grows in the high pastures. Shallot is consumed in dry areas in most parts of the country. Also shallots have been well known in Iranian folk medicine and its bulbs have been widely used for treating rheumatic and inflammatory disorders. In addition, this plant is used in the preparation of significant amounts of potassium, phosphorus, calcium, magnesium, sodium, pickles and as an additive to yogurt and pickles. ANN as a modern approach has successfully been used to solve an extensive variety of problems in the science and engineering, exclusively for some space where the conventional modeling procedure fail. A well-trained ANN can be used as a predictive model for a special use, which is a data processing system inspired by biological neural system. When mathematical equations are difficult to extrapolate, and fuzzy logic is better when decisions must be made with the estimated values below the incomplete information. The fuzzy logic theory effectively addresses the uncertainty problems that solve the ambiguity. The aim of this study was to predict moisture ratio of garlic and shallot during the drying process with fluidized bed dryer using mathematical model, artificial neural networks and fuzzy logic methods. Tests were carried out on three levels of inlet air temperature (40, 55 and 70 °C) and three inlet air velocities (0.5, 1.5 and 2.5 m/s). To estimate the drying kinetic of garlic and shallot, five mathematical models were used to fit the experimental data of thin layer drying. Three factors (air temperature, air velocity and drying time) to forecast moisture ratio in fluidized bed dryer as independent variables for artificial neural networks and fuzzy logic was considered. Cascade forward back propagation (CFBP) and feed forward back propagation (FFBP) with Levenberg-Marquardt (LM), Bayesian learning (BR) algorithms for ANN and the Mamdani Fuzzy Inference System using triangular membership function were used for training patterns. Consequently, the Page and Midilli et al. model was selected as the best mathematical model to describe the drying kinetics of the garlic and shallot slices, respectively. The results of artificial neural networks model for predicting MR showed that the R2 of 0.9994 and 0.9996; and and RMSE of 0.0036 and 0.0014 were obtained for garlic and shallot, respectively. Also, The fuzzy inference system presented the R2 of 0.9997 and 0.9998; and and RMSE of 0.0027 and 0.0011 for garlic and shallot, respectively. Comparing the results obtained from mathematical models, artificial neural networks and fuzzy logic, showed that the RMSE in the fuzzy logic was lower than artificial neural network and mathematical models. Three factors (air temperature, air velocity and drying time) were considered for forecasting moisture ratio in fluidized bed dryer as independent variables using mathematical model, artificial neural networks and fuzzy logic. Cascade forward back propagation (CFBP) and feed forward back propagation (FFBP) with Levenberg-Marquardt (LM), Bayesian learning (BR) algorithms and the Mamdani Fuzzy Inference System using triangular membership function were used for training the patterns. Comparing the results obtained from mathematical models, artificial neural networks and fuzzy logic, showed that the root mean square error in fuzzy logic was lower than others.

Downy Mildew of cucurbits is one of the most important diseases of cucumber in humid areas and greenhouses. It can lead to significant damages to the quality and quantity of the product, if not diagnosed on time. In this study, the possibility of using image processing for determining the downy mildew of greenhouse cucumber was investigated. The captured images from cucumber leaves at several stages of disease severity were processed in Image Processing toolbox of MATLAB programming software. Color images were transferred to several color spaces and then color components were examined by discriminant analysis. Cr color component was determined to be suitable to detect disease spots in leaf and was used to develop the recognition algorithm. The accuracy of algorithm in terms of identify the infected areas of leaves was 97.4±1.4 percent. Discriminant analysis was also used to classify the severity of the disease. Results revealed that image processing is a suitable method for accurate diagnosis of downy mildew in greenhouse cucumber leaves. Discriminant analysis is also a useful tool to classify disease severity in images resulted from image processing.